Modern aircraft are increasingly complex. The complexities of these aircraft have led to an increasing need for automated fault detection systems. These fault detection systems are designed to monitor the various systems of the aircraft in an effect to detect potential faults. These systems are designed to detect these potential faults such that the potential faults can be addressed before the potential faults lead to serious system failure and possible in-flight shutdowns, take-off aborts, and delays or cancellations.
Engines are, of course, a particularly critical part of the aircraft. As such, fault detection for aircraft engines are an important part of an aircraft's fault detection system. Some traditional engine fault detection has been limited to methods of fault detection that are based on linear relationships between variables in the system. While these methods have been effective in detecting some faults, they are less effective in detecting faults in systems where there are significant nonlinearities in the system. Many complex systems, such as turbine engines, have substantially nonlinear relationships between variables in the system. In these types of system, the nonlinear relationship between variables reduces the effectiveness of these linear techniques for fault detection.
Thus, what is needed is an improved system and method for fault detection that is able to detect and classify fault in systems with nonlinear relationships among variables or observed measurements.